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Paper Citation Record · LEDGER

One Framework for All: Cross-Modal Membership Inference for Generative Models

As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2607.04339.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.04339 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T19:58:05.484186Z

measured 57 of 57 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

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Reference resolution

57 of 57 outbound references displayed

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Outbound references

Observation a4766f32-1946-48c3-8fdf-b986a1e80c1a · outbound

This paper cites LLMGA: Multimodal Large Language Model based Generation Assistant,.

One Framework for All: Cross-Modal Membership Inference for Generative Models LLMGA: Multimodal Large Language Model based Generation Assistant,

Reference 1

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Observation 6d5c157c-e03e-4b40-8867-6a72e6263375 · outbound

This paper cites Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Evaluating Multimodal Language Models as Visual Assistants for Visually Impaired Users,

Reference 2

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Observation f5eabeaf-3377-4fa5-afcf-2cbab79e484c · outbound

This paper cites Membership Inference Attacks Against Machine Learn- ing Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Membership Inference Attacks Against Machine Learn- ing Models,

Reference 3

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Observation 904ce322-1d04-450b-a52b-182274ee1f6a · outbound

This paper cites Practical Member- ship Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Practical Member- ship Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study,

Reference 4

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Observation 551f520f-bc45-4c1f-a2b1-ef0f50b38d8c · outbound

This paper cites Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training,

Reference 5

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Observation 915a9789-b55e-44ea-9a96-234b1a538f4e · outbound

This paper cites Practical Membership Inference Attacks against Fine- tuned Large Language Models via Self-prompt Calibra- tion,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Practical Membership Inference Attacks against Fine- tuned Large Language Models via Self-prompt Calibra- tion,

Reference 6

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Observation 8c0bbc42-f937-40c1-a231-093d204bf17c · outbound

This paper cites Black-box Membership Inference Attacks against Fine-tuned Diffusion Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Black-box Membership Inference Attacks against Fine-tuned Diffusion Models,

Reference 7

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Observation 66d894b2-c333-4d7b-8492-1b630e5c1b2d · outbound

This paper cites Membership Inference Attacks Against Vision- Language Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Membership Inference Attacks Against Vision- Language Models,

Reference 8

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Observation 4d47b7c1-bca3-4686-adad-0f7d5db1f638 · outbound

This paper cites MM-LLMs: Recent Advances in Mul- tiModal Large Language Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models MM-LLMs: Recent Advances in Mul- tiModal Large Language Models,

Reference 9

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Observation 4eec1c25-f847-442c-bfef-7dd5adc7dfdb · outbound

This paper cites Gen- erative Multimodal Models are In-Context Learners,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Gen- erative Multimodal Models are In-Context Learners,

Reference 10

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Observation 9de63052-1aa8-4e01-b657-e62e52f09f60 · outbound

This paper cites GPT-4 Technical Report.

One Framework for All: Cross-Modal Membership Inference for Generative Models GPT-4 Technical Report

Reference 11

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Observation bb2d22f2-c5a9-4e55-94b0-7fdca6a0bc5f · outbound

This paper cites Dif- fusion Models in Vision: A Survey,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Dif- fusion Models in Vision: A Survey,

Reference 12

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Observation 087b1474-fbb6-48b1-b49b-86370a149348 · outbound

This paper cites Vision-Language Models for Vision Tasks: A Survey,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Vision-Language Models for Vision Tasks: A Survey,

Reference 13

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Observation 1ed80c0a-d643-422c-81ae-dc3de19bf409 · outbound

This paper cites ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classi- fication and Localization of Common Thorax Diseases,.

One Framework for All: Cross-Modal Membership Inference for Generative Models ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classi- fication and Localization of Common Thorax Diseases,

Reference 14

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Observation c19675db-c8be-4fe2-98fd-32e726c5ce1a · outbound

This paper cites BLIP: Bootstrap- ping Language-Image Pre-training for Unified Vision- Language Understanding and Generation,.

One Framework for All: Cross-Modal Membership Inference for Generative Models BLIP: Bootstrap- ping Language-Image Pre-training for Unified Vision- Language Understanding and Generation,

Reference 15

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Observation 632042fb-f714-4b4b-8875-c6f997d78ca9 · outbound

This paper cites A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks,.

One Framework for All: Cross-Modal Membership Inference for Generative Models A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks,

Reference 16

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Observation 7fe9e188-4542-442a-a08d-b62ddcce6a22 · outbound

This paper cites Visualizing Data using t-SNE,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Visualizing Data using t-SNE,

Reference 17

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Observation 437295a7-866d-412d-8035-ec4ed092175b · outbound

This paper cites Language Models are Unsupervised Multitask Learners,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Language Models are Unsupervised Multitask Learners,

Reference 18

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Observation 329fd2b5-1afc-47a2-940b-818ce6fb7500 · outbound

This paper cites The Falcon Series of Open Language Models.

One Framework for All: Cross-Modal Membership Inference for Generative Models The Falcon Series of Open Language Models

Reference 19

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Observation 6c8bfdca-57f1-4df6-96dc-2a7426414831 · outbound

This paper cites Pointer Sentinel Mixture Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Pointer Sentinel Mixture Models,

Reference 20

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Observation 05da9bde-187e-462d-9e90-4504c4d1cb96 · outbound

This paper cites Don’t Give Me the Details, Just the Summary! Topic-Aware Convo- lutional Neural Networks for Extreme Summarization,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Don’t Give Me the Details, Just the Summary! Topic-Aware Convo- lutional Neural Networks for Extreme Summarization,

Reference 21

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Observation 96c5232a-62f6-49d7-ae40-05fb1faa9978 · outbound

This paper cites Dis- tilBERT, a Distilled Vrsion of BERT: Smaller, Faster, Cheaper and Lighter,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Dis- tilBERT, a Distilled Vrsion of BERT: Smaller, Faster, Cheaper and Lighter,

Reference 22

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Observation 791bebfa-ecd7-4dc4-a224-07eac723471b · outbound

This paper cites High-Resolution Image Synthesis with La- tent Diffusion Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models High-Resolution Image Synthesis with La- tent Diffusion Models,

Reference 23

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Observation df6b8ba3-ca9d-4471-8663-1394aad67452 · outbound

This paper cites LAION-5B: An open large- scale dataset for training next generation image-text models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models LAION-5B: An open large- scale dataset for training next generation image-text models,

Reference 24

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Observation 37cfce95-0c17-46b0-a9b3-a23d0948ff20 · outbound

This paper cites Microsoft COCO: Common Objects in Context,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Microsoft COCO: Common Objects in Context,

Reference 25

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Observation d00e42ee-d5ed-4794-980b-2b83e8f7a963 · outbound

This paper cites Talk- to-Edit: Fine-Grained Facial Editing via Dialog,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Talk- to-Edit: Fine-Grained Facial Editing via Dialog,

Reference 26

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Observation beecac79-f0fe-43e2-970d-b11b42d9c11b · outbound

This paper cites Visual Instruction Tuning,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Visual Instruction Tuning,

Reference 27

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Observation 412913be-985e-4ace-97a1-136fedef8baa · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understand- ing with Advanced Large Language Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models MiniGPT-4: Enhancing Vision-Language Understand- ing with Advanced Large Language Models,

Reference 28

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Observation 8d16249b-33af-4131-8a9c-3f9a85f9ab40 · outbound

This paper cites all-MiniLM-L6-v2,.

One Framework for All: Cross-Modal Membership Inference for Generative Models all-MiniLM-L6-v2,

Reference 29

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Observation c7989370-2823-4b22-bb63-d1c5d8926111 · outbound

This paper cites Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble,

Reference 30

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Observation 3dad1f09-c450-4708-8326-a9f41df5381a · outbound

This paper cites In-Context Probing for Membership Inference in Fine-Tuned Language Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models In-Context Probing for Membership Inference in Fine-Tuned Language Models,

Reference 31

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Observation f8cdaf82-78de-49db-baf4-897dbcbc3789 · outbound

This paper cites Membership Inference on Text- to-Image Diffusion Models via Conditional Likelihood Discrepancy,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Membership Inference on Text- to-Image Diffusion Models via Conditional Likelihood Discrepancy,

Reference 32

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Observation ec57107f-7873-435a-aa29-c014e526aa7d · outbound

This paper cites Membership Inference Attacks against Large Vision-Language Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Membership Inference Attacks against Large Vision-Language Models,

Reference 33

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Observation 841f5f3d-199b-46fb-991f-ae8f51d3fe9c · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

One Framework for All: Cross-Modal Membership Inference for Generative Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 34

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Observation 32837a37-b408-4ae6-9442-6e54b233b313 · outbound

This paper cites Visual Transformers: Token-based Image Representation and Processing for Computer Vision.

One Framework for All: Cross-Modal Membership Inference for Generative Models Visual Transformers: Token-based Image Representation and Processing for Computer Vision

Reference 35

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Observation 60e442f5-6f9f-4ae1-b059-f5a561c3cb1c · outbound

This paper cites Model card: Clip.

One Framework for All: Cross-Modal Membership Inference for Generative Models Model card: Clip

Reference 36

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Observation 5ac44ec5-8e0d-49b1-b87d-93553b003102 · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

One Framework for All: Cross-Modal Membership Inference for Generative Models ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 37

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Observation 442b704c-4f79-431f-94f0-3f9fd91b5da1 · outbound

This paper cites all-mpnet-base-v2.

One Framework for All: Cross-Modal Membership Inference for Generative Models all-mpnet-base-v2

Reference 38

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Observation 0529312f-1de0-4409-9f68-8f045adea5bf · outbound

This paper cites Guided-diffusion.

One Framework for All: Cross-Modal Membership Inference for Generative Models Guided-diffusion

Reference 39

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Observation 8f81655b-ce9b-4385-a41e-6106ed355f18 · outbound

This paper cites Deep Learning with Differential Privacy,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Deep Learning with Differential Privacy,

Reference 40

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Observation 22fc4441-acb5-45b2-94ed-fd92b46af84f · outbound

This paper cites Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splitting.

One Framework for All: Cross-Modal Membership Inference for Generative Models Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splitting

Reference 41

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Observation 2edaa61a-7d2a-4017-a0fd-65c6abccd9d8 · outbound

This paper cites Comprehen- sive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Comprehen- sive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning,

Reference 42

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Observation 51397cb2-acc3-4a9c-aedd-447c8dd707a4 · outbound

This paper cites Enhanced Membership Inference Attacks against Machine Learning Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Enhanced Membership Inference Attacks against Machine Learning Models,

Reference 43

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Observation 6a5b00fc-7a33-45aa-8f51-cbd517db9564 · outbound

This paper cites Membership Inference Attacks on Machine Learning: A Survey,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Membership Inference Attacks on Machine Learning: A Survey,

Reference 44

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Observation 9c2e248b-7be2-42d8-8f82-e3640aaa5c36 · outbound

This paper cites Label-Only Membership Inference Attacks,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Label-Only Membership Inference Attacks,

Reference 45

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Observation c668aaeb-e722-49c6-9907-7a2a639e2ca3 · outbound

This paper cites Membership Leakage in Label-Only Exposures,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Membership Leakage in Label-Only Exposures,

Reference 46

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Observation 1926a7bc-d4b0-4979-a952-2fd2e48653a4 · outbound

This paper cites You Only Query Once: An Efficient Label-Only Membership Inference Attack,.

One Framework for All: Cross-Modal Membership Inference for Generative Models You Only Query Once: An Efficient Label-Only Membership Inference Attack,

Reference 47

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Observation 62128e9f-fada-42aa-be9f-8ca6feacd772 · outbound

This paper cites Enhanced Label-Only Membership Inference Attacks with Fewer Queries,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Enhanced Label-Only Membership Inference Attacks with Fewer Queries,

Reference 48

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source=pdf_text observed=2026-07-11T19:58:05.484186Z digest=sha256:9e161c0cdf7b9b3d31f21ce0be4e7253cde9ba864484d1b4043d331372039ce9

Observation 3fceec28-1c80-46b3-b53d-482a31840628 · outbound

This paper cites Do Membership Inference Attacks Work on Large Language Models?.

One Framework for All: Cross-Modal Membership Inference for Generative Models Do Membership Inference Attacks Work on Large Language Models?

Reference 49

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Observation 73f53585-faf1-499f-8c1f-926d1022f411 · outbound

This paper cites Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models,

Reference 50

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Observation 43dd02de-6efe-4804-b7d5-abfbfb427617 · outbound

This paper cites DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation,.

One Framework for All: Cross-Modal Membership Inference for Generative Models DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation,

Reference 51

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Observation 6b5651fd-d25c-4ba3-8174-13425e72248d · outbound

This paper cites Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation,

Reference 52

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Observation 4aa0aafb-20d5-41ad-a1cc-17393a1e87a8 · outbound

This paper cites Generated Distributions Are All You Need for Mem- bership Inference Attacks Against Generative Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Generated Distributions Are All You Need for Mem- bership Inference Attacks Against Generative Models,

Reference 53

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Observation bab82faa-555e-49ed-aa34-f7891a9aa64d · outbound

This paper cites Variance-Based Membership Inference Attacks Against Large-Scale Image Captioning Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Variance-Based Membership Inference Attacks Against Large-Scale Image Captioning Models,

Reference 54

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Observation e4d4fae8-df58-4d38-b5e3-6a41108d4ea5 · outbound

This paper cites Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models,

Reference 55

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Observation 64452207-df78-49ed-87fc-76457f119b80 · outbound

This paper cites Towards Label-Only Membership Inference Attack against Pre-trained Large Language Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Towards Label-Only Membership Inference Attack against Pre-trained Large Language Models,

Reference 56

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Observation e156f1bd-8e08-4b00-b01e-76a64b6a75c0 · outbound

This paper cites Context-Aware Membership Inference Attacks against Pre-trained Large Language Models,.

One Framework for All: Cross-Modal Membership Inference for Generative Models Context-Aware Membership Inference Attacks against Pre-trained Large Language Models,

Reference 57

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